@InProceedings{yates-cohan-goharian:2017:EMNLP2017,
  author    = {Yates, Andrew  and  Cohan, Arman  and  Goharian, Nazli},
  title     = {Depression and Self-Harm Risk Assessment in Online Forums},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {2968--2978},
  abstract  = {Users suffering from mental health conditions often turn to online resources
	for support, including specialized online support communities or general
	communities such as Twitter and Reddit. In this work, we present a framework
	for supporting and studying users in both types of communities. We propose
	methods for identifying posts in support communities that may indicate a risk
	of self-harm, and demonstrate that our approach outperforms strong previously
	proposed methods for identifying such posts. Self-harm is closely related to
	depression, which makes identifying depressed users on general forums a crucial
	related task. We introduce a large-scale general forum dataset consisting of
	users with self-reported depression diagnoses matched with control users. We
	show how our method can be applied to effectively identify depressed users from
	their use of language alone. We demonstrate that our method outperforms strong
	baselines on this general forum dataset.},
  url       = {https://www.aclweb.org/anthology/D17-1322}
}